Research Scientist, Spatial Intelligence - New College Grad 2025

Details

NVIDIA is seeking a Research Scientist to join the Spatial Intelligence research team to conduct cutting-edge research at the intersection of Machine Learning, Computer Vision and Computer Graphics. The role focuses on advancing AI for simulation, 3D deep learning, DL for animation, content generation, transfer learning, domain adaptation, computer vision, and medical imaging, with opportunities to integrate research into simulation systems, digital avatars, robotics and other NVIDIA platforms.

Responsibilities

• Develop and optimize diffusion-based generative models for high-fidelity, controllable image/video synthesis.
• Design scalable architectures that reduce inference latency and enable real-time or interactive generation workflows.
• Contribute to fundamental research and publish in top-tier conferences such as CVPR, NeurIPS, ICLR, ICCV, or SIGGRAPH.
• Collaborate across teams to integrate research into simulation systems, digital avatars, robotics, and other NVIDIA platforms.

Requirements

• PhD or equivalent experience in Computer Science, Computer Vision, Machine Learning, or a related field.
• Strong foundation in generative models.
• Experience with Python and PyTorch, and large-scale model training workflows.
• Ability to conduct independent research and communicate findings clearly.
• Passion for innovation in vision AI and a drive to move ideas from research to impact.

Ways to stand out

• Skills in efficient model architectures and efficient implementations.
• Sharp mathematics skills.

Benefits and Additional Information

• Base salary range (location- and experience-dependent): 198,750 CAD - 344,500 CAD.
• Eligible for equity and company benefits.
• Applications for this job will be accepted at least until July 29, 2025.

Technologies and Topics Mentioned

Machine learning, computer vision, computer graphics, diffusion models, generative models, 3D deep learning, DL for animation, content generation, transfer learning, domain adaptation, medical imaging, simulation, robotics, Python, PyTorch, large-scale model training, scalable architectures, real-time inference, efficient model architectures, mathematics.